The demands for real-time situational awareness continue to expand from monitoring activities during emergencies, such as at the scene of a disaster, a terrorist activity, or a situation demanding emergency intervention by medical, police or fire personnel. These responses are often based on limited data sent from a single source, but for complex situations a range of more intensive data acquisition efforts are needed to monitor conditions and generate warnings of potential safety or security concerns. Statistically based applications which issue notifications or interrupts when a danger is imminent sometimes require multi-channel sensing followed by analyses such as object classification. Systems performing comprehensive monitoring often require generating multiple data types and may be tasked with reporting several fields of data (e.g., object type, position and movement or coordinate information) to reliably assess safety and security concerns. Providing more comprehensive performance in these systems can increase cost as well as complexity of both data acquisition and processing. This is especially true when large amounts of data must be acquired during short time intervals, e.g., fractions of seconds, and rapidly processed for visual displays or other forms of reporting media.
Use of vision systems in the field of highway traffic safety is illustrative of the need for more extensive data acquisition and comprehensive monitoring to rapidly react to unpredictable roadway dynamics. Existing vision solutions to increase awareness of vehicle surroundings have included combinations of cameras and other technologies (e.g., RADAR or LiDAR systems) to create dynamic maps of vehicle surroundings. LiDAR technology holds potential for achieving a comprehensive solution which combines multiple channels of both video and laser-based mapping data for self-driving vehicles. If the relatively high cost of Lidar systems declines significantly, this technology is more likely to be more widely deployed in automotive applications. Presently, assimilating and processing such high speed data rates for responses remains a costly challenge for self-driving vehicles, as rapid and reliable detection of surrounding activities is requisite for real-time response capabilities.
For driver-operated vehicles there is a need for lower cost sensing of nearby vehicles, pedestrians and traffic control signals to promptly generate warnings, enhance in-vehicle driver information, and even take control of a vehicle to avoid an accident. Information needed to make rapid assessments and interventions for vehicle safety requires rapid processing capability of large amounts of data from a relatively large number of sensors. Otherwise, issuing notifications and interventions to avert potential problems would be untimely or unreliable. It is often desirable to create a comprehensive awareness of vehicle surroundings, to avoid potential hazards, and to assure rapid response times for incident avoidance.
Generally, the cost of hardware that rapidly acquires and processes large amounts of data for real-time responses renders camera-based vision systems expensive, power consumptive and difficult to deploy in, for example, the automotive market. Simpler and lower cost solutions are needed to create vision systems which provide real-time responses for improved traffic safety.